University of Electronic Science and Technology of China

About: University of Electronic Science and Technology of China is a education organization based out in Chengdu, China. It is known for research contribution in the topics: Antenna (radio) & Dielectric. The organization has 50594 authors who have published 58502 publications receiving 711188 citations. The organization is also known as: UESTC.

Rare-Earth Single-Atom La-N Charge-Transfer Bridge on Carbon Nitride for Highly Efficient and Selective Photocatalytic CO2 Reduction.

Abstract: Photocatalytic CO2 conversion into valuable solar fuels is highly appealing, but lack of directional charge-transfer channel and insufficient active sites resulted in limited CO2 reduction efficiency and selectivity for most photocatalytic systems. Herein, we designed and fabricated rare-earth La single-atoms on carbon nitride with La-N charge-transfer bridge as the active center for photocatalytic CO2 reaction. The formation of La single-atoms was certified by spherical aberration-corrected HAADF-STEM, STEM-EELS, EXAFS, and theoretical calculations. The electronic structure of the La-N bridge enables a high CO-yielding rate of 92 μmol·g-1·h-1 and CO selectivity of 80.3%, which is superior to most g-C3N4-based photocatalytic CO2 reductions. The CO production rate remained nearly constant under light irradiation for five cycles of 20 h, indicating its stability. The closely combined experimental and DFT calculations clearly elucidated that the variety of electronic states induced by 4f and 5d orbitals of the La single atom and the p-d orbital hybridization of La-N atoms enabled the formation of charge-transfer channel. The La-N charge bridges are found to function as the key active center for CO2 activation, rapid COOH* formation, and CO desorption. The present work would provide a mechanistic understanding into the utilization of rare-earth single-atoms in photocatalysis for solar energy conversion.

A generalized mean distance-based k-nearest neighbor classifier

Abstract: K-nearest neighbor (KNN) rule is a well-known non-parametric classifier that is widely used in pattern recognition. However, the sensitivity of the neighborhood size k always seriously degrades the KNN-based classification performance, especially in the case of the small sample size with the existing outliers. To overcome this issue, in this article we propose a generalized mean distance-based k-nearest neighbor classifier (GMDKNN) by introducing multi-generalized mean distances and the nested generalized mean distance that are based on the characteristic of the generalized mean. In the proposed method, multi-local mean vectors of the given query sample in each class are calculated by adopting its class-specific k nearest neighbors. Using the achieved k local mean vectors per class, the corresponding k generalized mean distances are calculated and then used to design the categorical nested generalized mean distance. In the classification phase, the categorical nested generalized mean distance is used as the classification decision rule and the query sample is classified into the class with the minimum nested generalized mean distance among all the classes. Extensive experiments on the UCI and KEEL data sets, synthetic data sets, the KEEL noise data sets and the UCR time series data sets are conducted by comparing the proposed method to the state-of-art KNN-based methods. The experimental results demonstrate that the proposed GMDKNN performs better and has the less sensitiveness to k. Thus, our proposed GMDKNN with the robust and effective classification performance could be a promising method for pattern recognition in some expert and intelligence systems.

Recent Advances in the Development of Water Oxidation Electrocatalysts at Mild pH

Abstract: Developing anodic oxygen evolution reaction (OER) electrocatalysts with high catalytic activities is of great importance for effective water splitting. Compared with the water-oxidation electrocatalysts that are commonly utilized in alkaline conditions, the ones operating efficiently under neutral or near neutral conditions are more environmentally friendly with less corrosion issues. This review starts with a brief introduction of OER, the importance of OER in mild-pH media, as well as the fundamentals and performance parameters of OER electrocatalysts. Then, recent progress of the rational design of electrocatalysts for OER in mild-pH conditions is discussed. The chemical structures or components, synthetic approaches, and catalytic performances of the OER catalysts will be reviewed. Some interesting insights into the catalytic mechanism are also included and discussed. It concludes with a brief outlook on the possible remaining challenges and future trends of neutral or near-neutral OER electrocatalysts. It hopefully provides the readers with a distinct perspective of the history, present, and future of OER electrocatalysts at mild conditions.

Reducing Mutual Coupling of Closely Spaced Microstrip MIMO Antennas for WLAN Application

Abstract: An efficient mutual coupling reduction method is introduced in this letter for extremely closely placed dual-element microstrip antennas positioned on a finite-sized ground plane for WLAN MIMO application at 5.8 GHz. High isolation can be achieved through a simple slot structure on the ground between the microstrip antennas. The position, length, and width of the slot have been optimized for maximizing the isolation. It is found that more than 40 dB isolation can be achieved between two parallel microstrip antennas sharing a common ground plane. The space distance of these antennas is 17.5 mm ≈ 0.33λ0 from element center to center (side by side of 1.6 mm ≈ 0.031λ0) when the ground plane size is 0.85λ0 × 0.55λ0. Along with this letter, several prototypes were fabricated, and their performances measured to validate the obtained IE3D moment method-based simulation results.